import pickle
import cv2
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import glob
%matplotlib qt

1. Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.

def Calib2Undistort(input_dir, src_file, output_dir, corners_x, corners_y):
    # prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
    objp = np.zeros((corners_x*corners_y,3), np.float32)
    objp[:,:2] = np.mgrid[0:corners_x, 0:corners_y].T.reshape(-1,2)

    # Arrays to store object points and image points from all the images.
    objpoints = [] # 3d points in real world space
    imgpoints = [] # 2d points in image plane.

    # Make a list of calibration images
    images = glob.glob(input_dir +'/'+ src_file)

    # Step through the list and search for chessboard corners
    for idx, fname in enumerate(images):
        img = cv2.imread(fname)
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

        # Find the chessboard corners
        ret, corners = cv2.findChessboardCorners(gray, (corners_x,corners_y), None)

        # If found, add object points, image points
        if ret == True:
            objpoints.append(objp)
            imgpoints.append(corners)

            # Draw and display the corners
            cv2.drawChessboardCorners(img, (corners_x,corners_y), corners, ret)
            _, _write_name = fname.split("\\")
            write_name = output_dir+'/dc_'+_write_name
            cv2.imwrite(write_name, img)
            
            #calibrate camera
            ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
            
            #undistort the image
            dst = cv2.undistort(img, mtx, dist, None, mtx)
            write_name = output_dir +'/ud_'+_write_name
            cv2.imwrite(write_name, dst)
    
    # Save the camera calibration result for later use (we won't worry about rvecs / tvecs)
    dist_pickle = {}
    dist_pickle["mtx"] = mtx
    dist_pickle["dist"] = dist
    pickle.dump( dist_pickle, open( "camera_cal/wide_dist_pickle.p", "wb" ) )
    

Calib2Undistort("camera_cal","calibration*.jpg","output_images",9,6)

2. Apply a distortion correction to raw images.

# Read in the saved objpoints and imgpoints
dist_pickle = pickle.load( open( "camera_cal/wide_dist_pickle.p", "rb" ) )
mtx = dist_pickle["mtx"]
dist = dist_pickle["dist"]
images = glob.glob("test_images/*.jpg")
   
# Step through the list and search for chessboard corners
for idx, fname in enumerate(images):
    img = plt.imread(fname)
   
    dst = cv2.undistort(img, mtx, dist, None, mtx)
    _, _write_name = fname.split("\\")
    write_name = "undistorted/"+_write_name
    plt.imsave(write_name, img)
    
    f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
    f.tight_layout()
    ax1.imshow(img)
    ax1.set_title('Original Image', fontsize=50)
    ax2.imshow(dst)
    ax2.set_title('Undistorted Image', fontsize=50)
    plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)

3. Use color transforms, gradients, etc., to create a thresholded binary image.

images = glob.glob("undistorted/*.jpg")
   
# Step through the list and search for chessboard corners
for idx, fname in enumerate(images):
    img = plt.imread(fname)
    
    # Convert to HLS color space and separate the S channel
    hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
    s_channel = hls[:,:,2]
    # Convert to HSV color space and separate the V channel
    hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
    v_channel = hsv[:,:,2]
    
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    
    # Sobel x
    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0) # Take the derivative in x
    abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal
    scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))
    
    # Threshold color channel S
    thresh_min = 50
    thresh_max = 255
    sxbinary = np.zeros_like(scaled_sobel)
    sxbinary[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1

    # Threshold color channel V
    v_thresh_min = 200
    v_thresh_max = 255
    v_binary = np.zeros_like(v_channel)
    v_binary[(v_channel >= v_thresh_min) & (v_channel <= v_thresh_max)] = 1

    # Stack each channel to view their individual contributions in green and blue respectively
    # This returns a stack of the two binary images, whose components you can see as different colors
    color_binary = np.dstack(( np.zeros_like(sxbinary), sxbinary, v_binary)) * 255

    # Combine the two binary thresholds
    combined_binary = np.zeros_like(sxbinary)
    combined_binary[(v_binary == 1) | (sxbinary == 1)] = 1
    
    _, _write_name = fname.split("\\")
    write_name = "s_gradient/"+_write_name
    plt.imsave(write_name, combined_binary, cmap='gray')

    # Plotting thresholded images
    f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
    ax1.set_title('Stacked thresholds')
    ax1.imshow(color_binary)

    ax2.set_title('Combined S channel and V channel thresholds')
    ax2.imshow(combined_binary, cmap='gray')

4. Apply a perspective transform to rectify binary image ("birds-eye view").

def region_of_interest(img, vertices):
    """
    Applies an image mask.
    
    Only keeps the region of the image defined by the polygon
    formed from `vertices`. The rest of the image is set to black.
    `vertices` should be a numpy array of integer points.
    """
    #defining a blank mask to start with
    mask = np.zeros_like(img)   
    
    #defining a 3 channel or 1 channel color to fill the mask with depending on the input image
    if len(img.shape) > 2:
        channel_count = img.shape[2]  # i.e. 3 or 4 depending on your image
        ignore_mask_color = (255,) * channel_count
    else:
        ignore_mask_color = 255
        
    #filling pixels inside the polygon defined by "vertices" with the fill color    
    cv2.fillPoly(mask, vertices, ignore_mask_color)
    
    #returning the image only where mask pixels are nonzero
    masked_image = cv2.bitwise_and(img, mask)
    return masked_image

sROI = [[210,720],[1100,720],[720,470],[565,470]]
dROI = [[320,720],[960,720],[960,0],[320,0]]

# Define a function that takes an image, number of x and y points, 
# camera matrix and distortion coefficients
def corners_unwarp(img, sROI, dROI):
    
    # Grab the image shape
    img_size = (img.shape[1], img.shape[0])
    
    # For source points I'm grabbing the outer four detected corners
    src = np.float32([sROI[0], sROI[1], sROI[2], sROI[3]])
    # For destination points, I'm arbitrarily choosing some points to be
    # a nice fit for displaying our warped result 
    # again, not exact, but close enough for our purposes
    dst = np.float32([dROI[0], dROI[1], dROI[2], dROI[3]])
    # Given src and dst points, calculate the perspective transform matrix
    M = cv2.getPerspectiveTransform(src, dst)
    # Warp the image using OpenCV warpPerspective()
    warped = cv2.warpPerspective(img, M, img_size,flags=cv2.INTER_LINEAR)

    # Return the resulting image and matrix
    return warped, M

# Read in the saved objpoints and imgpoints
dist_pickle = pickle.load( open( "camera_cal/wide_dist_pickle.p", "rb" ) )
mtx = dist_pickle["mtx"]
dist = dist_pickle["dist"]
images = glob.glob("s_gradient/*.jpg")
   
# Step through the list and search for chessboard corners
for idx, fname in enumerate(images):
    img = plt.imread(fname)
    #cv2.fillPoly(img, np.array([[(210,720),(1100,720),(720,470),(565,470)]]), 255)
    img_masked = region_of_interest(img, np.array([[(210-20, 720), (1100+20, 720),(720+10, 470-10),(565-10,470-10)]]))
    warped, M = corners_unwarp(img_masked, sROI, dROI)
    
    _, _write_name = fname.split("\\")
    write_name = "b_warped/"+_write_name
    plt.imsave(write_name, warped, cmap='gray')
    
    # Plotting thresholded images
    f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
    ax1.set_title('Combined S channel and gradient thresholds')
    ax1.imshow(img, cmap='gray')

    ax2.set_title('Perspective Transformed')
    ax2.imshow(warped, cmap='gray')

5. Detect lane pixels and fit to find the lane boundary.

def find_lane_pixels(binary_warped):
    # Take a histogram of the bottom half of the image
    histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
    # Create an output image to draw on and visualize the result
    out_img = np.dstack((binary_warped, binary_warped, binary_warped))
    # Find the peak of the left and right halves of the histogram
    # These will be the starting point for the left and right lines
    midpoint = np.int(histogram.shape[0]//2)
    leftx_base = np.argmax(histogram[:midpoint])
    rightx_base = np.argmax(histogram[midpoint:]) + midpoint

    # HYPERPARAMETERS
    # Choose the number of sliding windows
    nwindows = 9
    # Set the width of the windows +/- margin
    margin = 100
    # Set minimum number of pixels found to recenter window
    minpix = 50

    # Set height of windows - based on nwindows above and image shape
    window_height = np.int(binary_warped.shape[0]//nwindows)
    # Identify the x and y positions of all nonzero pixels in the image
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    # Current positions to be updated later for each window in nwindows
    leftx_current = leftx_base
    rightx_current = rightx_base

    # Create empty lists to receive left and right lane pixel indices
    left_lane_inds = []
    right_lane_inds = []

    # Step through the windows one by one
    for window in range(nwindows):
        # Identify window boundaries in x and y (and right and left)
        win_y_low = binary_warped.shape[0] - (window+1)*window_height
        win_y_high = binary_warped.shape[0] - window*window_height
        ### TO-DO: Find the four below boundaries of the window ###
        win_xleft_low = leftx_current - margin  # Update this
        win_xleft_high = leftx_current + margin  # Update this
        win_xright_low = rightx_current - margin  # Update this
        win_xright_high = rightx_current + margin  # Update this
        
        # Draw the windows on the visualization image
        #cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2)
        #cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2) 
        
        ### TO-DO: Identify the nonzero pixels in x and y within the window ###
        good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & 
        (nonzerox >= win_xleft_low) &  (nonzerox < win_xleft_high)).nonzero()[0]
        good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & 
        (nonzerox >= win_xright_low) &  (nonzerox < win_xright_high)).nonzero()[0]
      
        # Append these indices to the lists
        left_lane_inds.append(good_left_inds)
        right_lane_inds.append(good_right_inds)
        
        ### TO-DO: If you found > minpix pixels, recenter next window ###
        ### (`right` or `leftx_current`) on their mean position ###
        #pass # Remove this when you add your function
        print((good_left_inds))
        if len(good_left_inds) > minpix:
            leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
        if len(good_right_inds) > minpix:        
            rightx_current = np.int(np.mean(nonzerox[good_right_inds]))

    # Concatenate the arrays of indices (previously was a list of lists of pixels)
    try:
        left_lane_inds = np.concatenate(left_lane_inds)
        right_lane_inds = np.concatenate(right_lane_inds)
    except ValueError:
        # Avoids an error if the above is not implemented fully
        pass

    # Extract left and right line pixel positions
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds] 
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]

    return leftx, lefty, rightx, righty, out_img


def fit_polynomial(binary_warped):
    # Find our lane pixels first
    leftx, lefty, rightx, righty, out_img = find_lane_pixels(binary_warped[:,:,0])

    ### TO-DO: Fit a second order polynomial to each using `np.polyfit` ###
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2)

    # Generate x and y values for plotting
    ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
    try:
        left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
        right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
    except TypeError:
        # Avoids an error if `left` and `right_fit` are still none or incorrect
        print('The function failed to fit a line!')
        left_fitx = 1*ploty**2 + 1*ploty
        right_fitx = 1*ploty**2 + 1*ploty

    ## Visualization ##
    # Colors in the left and right lane regions
    out_img[lefty, leftx] = [255, 0, 0]
    out_img[righty, rightx] = [0, 0, 255]

    # Plots the left and right polynomials on the lane lines
    #plt.plot(left_fitx, ploty, color='yellow')
    #plt.plot(right_fitx, ploty, color='yellow')

    return out_img, left_fit, right_fit, left_fitx, right_fitx

left_fit = np.array([ 2.13935315e-04, -3.77507980e-01,  4.76902175e+02])
right_fit = np.array([4.17622148e-04, -4.93848953e-01,  1.11806170e+03])


def fit_poly(img_shape, leftx, lefty, rightx, righty):
    global left_fit, right_fit
    
    ### TO-DO: Fit a second order polynomial to each with np.polyfit() ###
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2)

    # Generate x and y values for plotting
    ploty = np.linspace(0, img_shape[0]-1, img_shape[0])
    ### TO-DO: Calc both polynomials using ploty, left_fit and right_fit ###
    left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
    right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
    
    return left_fitx, right_fitx, ploty

def search_around_poly(binary_warped):
    # HYPERPARAMETER
    # Choose the width of the margin around the previous polynomial to search
    # The quiz grader expects 100 here, but feel free to tune on your own!
    margin = 100

    # Grab activated pixels
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    
    ### TO-DO: Set the area of search based on activated x-values ###
    ### within the +/- margin of our polynomial function ###
    ### Hint: consider the window areas for the similarly named variables ###
    ### in the previous quiz, but change the windows to our new search area ###
    left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + 
                    left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) + 
                    left_fit[1]*nonzeroy + left_fit[2] + margin)))
    right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + 
                    right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) + 
                    right_fit[1]*nonzeroy + right_fit[2] + margin)))
    
    # Again, extract left and right line pixel positions
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds] 
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]

    # Fit new polynomials
    left_fitx, right_fitx, ploty = fit_poly(binary_warped.shape, leftx, lefty, rightx, righty)
    
    ## Visualization ##
    # Create an image to draw on and an image to show the selection window
    out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
    window_img = np.zeros_like(out_img)
    # Color in left and right line pixels
    out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
    out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]

    # Generate a polygon to illustrate the search window area
    # And recast the x and y points into usable format for cv2.fillPoly()
    left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
    left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, 
                              ploty])))])
    left_line_pts = np.hstack((left_line_window1, left_line_window2))
    right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
    right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, 
                              ploty])))])
    right_line_pts = np.hstack((right_line_window1, right_line_window2))

    # Draw the lane onto the warped blank image
    cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
    cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
    result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
    
    # Plot the polynomial lines onto the image
    #plt.plot(left_fitx, ploty, color='yellow')
    #plt.plot(right_fitx, ploty, color='yellow')
    ## End visualization steps ##
    
    return result, left_fit, right_fit, left_fitx, right_fitx

images = glob.glob("b_warped/*.jpg")
   
# Step through the list and search for chessboard corners
for idx, fname in enumerate(images):
    img = plt.imread(fname)
    img_fitted, left_fit, right_fit, left_fitx, right_fitx = search_around_poly(img[:,:,0])
    
    _, _write_name = fname.split("\\")
    write_name = "polyfit/"+_write_name
    plt.imsave(write_name, img_fitted, cmap='gray')
    
    # Plotting thresholded images
    f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
    ax1.set_title('Combined S channel and gradient thresholds')
    ax1.imshow(img, cmap='gray')

    ax2.set_title('PolyFit')
    ax2.imshow(img_fitted, cmap='gray')
    ploty = np.linspace(0, img.shape[0]-1, img.shape[0] )
    ax2.plot(left_fitx, ploty, color='yellow')
    ax2.plot(right_fitx, ploty, color='yellow')

6. Determine the curvature of the lane and vehicle position with respect to center.

def measure_curvature_real(img, left_fit, right_fit):
    '''
    Calculates the curvature of polynomial functions in meters.
    '''
    # Define conversions in x and y from pixels space to meters
    ym_per_pix = 30/720 # meters per pixel in y dimension
    xm_per_pix = 3.7/700 # meters per pixel in x dimension
    
    # Start by generating our fake example data
    # Make sure to feed in your real data instead in your project!
    #ploty, left_fit_cr, right_fit_cr = generate_data(ym_per_pix, xm_per_pix)
    ploty = np.linspace(0, img.shape[0]-1, img.shape[0])
    
    # Define y-value where we want radius of curvature
    # We'll choose the maximum y-value, corresponding to the bottom of the image
    y_eval = np.max(ploty)
    
    ##### TO-DO: Implement the calculation of R_curve (radius of curvature) #####
    left_curverad = (0.5 / np.abs(left_fit[0]))*(1+(2*left_fit[0]*y_eval*ym_per_pix+left_fit[1])**2)**1.5
    right_curverad = (0.5 / np.abs(right_fit[0]))*(1+(2*right_fit[0]*y_eval*ym_per_pix+right_fit[1])**2)**1.5
    
    # Calculate offset
    offset = ((left_fit[-1]+right_fit[-1])/2 - img.shape[1]/2) * xm_per_pix
    
    return left_curverad, right_curverad, offset

7. Warp the detected lane boundaries back onto the original image.

8. Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.

images = glob.glob("b_warped/*.jpg")
   
# Step through the list and search for chessboard corners
for idx, fname in enumerate(images):
    warped = plt.imread(fname)[:,:,0]
    img_fitted, left_fit, right_fit, left_fitx, right_fitx = search_around_poly(warped)
    
    _, f = fname.split("\\")
    undist = plt.imread("undistorted/"+f)
    # Create an image to draw the lines on
    warp_zero = np.zeros_like(warped).astype(np.uint8)
    color_warp = np.dstack((warp_zero, warp_zero, warp_zero))

    # Recast the x and y points into usable format for cv2.fillPoly()
    pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
    pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
    pts = np.hstack((pts_left, pts_right))

    # Draw the lane onto the warped blank image
    cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
    
    # Compute the inverse perspective transform:
    src = np.float32([sROI[0], sROI[1], sROI[2], sROI[3]])
    dst = np.float32([dROI[0], dROI[1], dROI[2], dROI[3]])
    Minv = cv2.getPerspectiveTransform(dst, src)
    
    # Warp the blank back to original image space using inverse perspective matrix (Minv)
    newwarp = cv2.warpPerspective(color_warp, Minv, (warped.shape[1], warped.shape[0])) 
    # Combine the result with the original image
    result = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)
    
    left_curverad, right_curverad, offset = measure_curvature_real(img_fitted, left_fit, right_fit)
    txt1 = "Vehicle Offset to Middle Lane = " + str(round(offset, 2)) + " m"
    if left_curverad < right_curverad:
        txt2 = "Radius of Lane = " + str(int(left_curverad)) + " m"
    else:
        txt2 = "Radius of Lane = " + str(int(right_curverad)) + " m"

    
    _, _write_name = fname.split("\\")
    write_name = "warped/"+_write_name
    plt.imsave(write_name, result)

    # Plotting thresholded images
    f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
    ax1.set_title('Undistorted')
    ax1.imshow(undist)

    ax2.set_title('Warped')
    ax2.imshow(result)
    plt.text(50, 50, txt1)
    plt.text(50, 100, txt2)